An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model

Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utiliz...

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Published inHuman genomics Vol. 17; no. 1; p. 80
Main Authors Charkoftaki, Georgia, Aalizadeh, Reza, Santos-Neto, Alvaro, Tan, Wan Ying, Davidson, Emily A, Nikolopoulou, Varvara, Wang, Yewei, Thompson, Brian, Furnary, Tristan, Chen, Ying, Wunder, Elsio A, Coppi, Andreas, Schulz, Wade, Iwasaki, Akiko, Pierce, Richard W, Cruz, Charles S Dela, Desir, Gary V, Kaminski, Naftali, Farhadian, Shelli, Veselkov, Kirill, Datta, Rupak, Campbell, Melissa, Thomaidis, Nikolaos S, Ko, Albert I, Thompson, David C, Vasiliou, Vasilis
Format Journal Article
LanguageEnglish
Published England BioMed Central 29.08.2023
BMC
Subjects
Accuracy
Allantoin
Biomarkers
Comorbidity
COVID-19
COVID-19 - epidemiology
Datasets
Discriminant analysis
Disease
Disease Outbreaks
Epidemics
Hospitalization
Hospitals
Humans
Infections
Leukocytes (eosinophilic)
Machine Learning
Mechanical ventilation
Metabolism
Metabolites
Metabolomics
Outbreaks
Patients
Plasma
Prediction models
Serotonin
Severe acute respiratory syndrome coronavirus 2
Triage
Tryptophan
Uric acid
White people
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Abstract Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R  = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.
AbstractList Abstract Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R 2  = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.
Abstract Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.
Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R  = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.
Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.
Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R 2  = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.
ArticleNumber 80
Author Thompson, David C
Chen, Ying
Wunder, Elsio A
Santos-Neto, Alvaro
Farhadian, Shelli
Cruz, Charles S Dela
Thompson, Brian
Vasiliou, Vasilis
Charkoftaki, Georgia
Aalizadeh, Reza
Campbell, Melissa
Tan, Wan Ying
Ko, Albert I
Iwasaki, Akiko
Coppi, Andreas
Pierce, Richard W
Datta, Rupak
Wang, Yewei
Desir, Gary V
Kaminski, Naftali
Veselkov, Kirill
Thomaidis, Nikolaos S
Schulz, Wade
Nikolopoulou, Varvara
Davidson, Emily A
Furnary, Tristan
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37641126$$D View this record in MEDLINE/PubMed
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Snippet Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated...
Abstract Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale,...
Abstract Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale,...
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StartPage 80
SubjectTerms Accuracy
Allantoin
Biomarkers
Comorbidity
COVID-19
COVID-19 - epidemiology
Datasets
Discriminant analysis
Disease
Disease Outbreaks
Epidemics
Hospitalization
Hospitals
Humans
Infections
Leukocytes (eosinophilic)
Machine Learning
Mechanical ventilation
Metabolism
Metabolites
Metabolomics
Outbreaks
Patients
Plasma
Prediction models
Serotonin
Severe acute respiratory syndrome coronavirus 2
Triage
Tryptophan
Uric acid
White people
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Title An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model
URI https://www.ncbi.nlm.nih.gov/pubmed/37641126
https://www.proquest.com/docview/2865407966/abstract/
https://search.proquest.com/docview/2858988753
https://pubmed.ncbi.nlm.nih.gov/PMC10463861
https://doaj.org/article/2f4a257343224b1581834ef8402d7d6e
Volume 17
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